# coding=utf-8
# Copyright 2019 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python2, python3
# coding=utf-8
"""Tokenization classes."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import re
import unicodedata
import six
from six.moves import range
import tensorflow as tf
import sentencepiece as spm

SPIECE_UNDERLINE = u'▁'.encode('utf-8')


def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
    """Checks whether the casing config is consistent with the checkpoint name."""

    # The casing has to be passed in by the user and there is no explicit check
    # as to whether it matches the checkpoint. The casing information probably
    # should have been stored in the bert_config.json file, but it's not, so
    # we have to heuristically detect it to validate.

    if not init_checkpoint:
        return

    m = re.match(
        '^.*?([A-Za-z0-9_-]+)/bert_model.ckpt', six.ensure_str(init_checkpoint)
    )
    if m is None:
        return

    model_name = m.group(1)

    lower_models = [
        'uncased_L-24_H-1024_A-16',
        'uncased_L-12_H-768_A-12',
        'multilingual_L-12_H-768_A-12',
        'chinese_L-12_H-768_A-12',
    ]

    cased_models = [
        'cased_L-12_H-768_A-12',
        'cased_L-24_H-1024_A-16',
        'multi_cased_L-12_H-768_A-12',
    ]

    is_bad_config = False
    if model_name in lower_models and not do_lower_case:
        is_bad_config = True
        actual_flag = 'False'
        case_name = 'lowercased'
        opposite_flag = 'True'

    if model_name in cased_models and do_lower_case:
        is_bad_config = True
        actual_flag = 'True'
        case_name = 'cased'
        opposite_flag = 'False'

    if is_bad_config:
        raise ValueError(
            'You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. '
            'However, `%s` seems to be a %s model, so you '
            'should pass in `--do_lower_case=%s` so that the fine-tuning matches '
            'how the model was pre-training. If this error is wrong, please '
            'just comment out this check.'
            % (
                actual_flag,
                init_checkpoint,
                model_name,
                case_name,
                opposite_flag,
            )
        )


def preprocess_text(inputs, remove_space = True, lower = False):
    """preprocess data by removing extra space and normalize data."""
    outputs = inputs
    if remove_space:
        outputs = ' '.join(inputs.strip().split())

    if six.PY2 and isinstance(outputs, str):
        try:
            outputs = six.ensure_text(outputs, 'utf-8')
        except UnicodeDecodeError:
            outputs = six.ensure_text(outputs, 'latin-1')

    outputs = unicodedata.normalize('NFKD', outputs)
    outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])
    if lower:
        outputs = outputs.lower()

    return outputs


def encode_pieces(sp_model, text, return_unicode = True, sample = False):
    """turn sentences into word pieces."""

    if six.PY2 and isinstance(text, six.text_type):
        text = six.ensure_binary(text, 'utf-8')

    if not sample:
        pieces = sp_model.EncodeAsPieces(text)
    else:
        pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)
    new_pieces = []
    for piece in pieces:
        piece = printable_text(piece)
        if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():
            cur_pieces = sp_model.EncodeAsPieces(
                six.ensure_binary(piece[:-1]).replace(SPIECE_UNDERLINE, b'')
            )
            if (
                piece[0] != SPIECE_UNDERLINE
                and cur_pieces[0][0] == SPIECE_UNDERLINE
            ):
                if len(cur_pieces[0]) == 1:
                    cur_pieces = cur_pieces[1:]
                else:
                    cur_pieces[0] = cur_pieces[0][1:]
            cur_pieces.append(piece[-1])
            new_pieces.extend(cur_pieces)
        else:
            new_pieces.append(piece)

    # note(zhiliny): convert back to unicode for py2
    if six.PY2 and return_unicode:
        ret_pieces = []
        for piece in new_pieces:
            if isinstance(piece, str):
                piece = six.ensure_text(piece, 'utf-8')
            ret_pieces.append(piece)
        new_pieces = ret_pieces

    return new_pieces


def encode_ids(sp_model, text, sample = False):
    pieces = encode_pieces(
        sp_model, text, return_unicode = False, sample = sample
    )
    ids = [sp_model.PieceToId(piece) for piece in pieces]
    return ids


def convert_to_unicode(text):
    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
    if six.PY3:
        if isinstance(text, str):
            return text
        elif isinstance(text, bytes):
            return six.ensure_text(text, 'utf-8', 'ignore')
        else:
            raise ValueError('Unsupported string type: %s' % (type(text)))
    elif six.PY2:
        if isinstance(text, str):
            return six.ensure_text(text, 'utf-8', 'ignore')
        elif isinstance(text, six.text_type):
            return text
        else:
            raise ValueError('Unsupported string type: %s' % (type(text)))
    else:
        raise ValueError('Not running on Python2 or Python 3?')


def printable_text(text):
    """Returns text encoded in a way suitable for print or `tf.logging`."""

    # These functions want `str` for both Python2 and Python3, but in one case
    # it's a Unicode string and in the other it's a byte string.
    if six.PY3:
        if isinstance(text, str):
            return text
        elif isinstance(text, bytes):
            return six.ensure_text(text, 'utf-8', 'ignore')
        else:
            raise ValueError('Unsupported string type: %s' % (type(text)))
    elif six.PY2:
        if isinstance(text, str):
            return text
        elif isinstance(text, six.text_type):
            return six.ensure_binary(text, 'utf-8')
        else:
            raise ValueError('Unsupported string type: %s' % (type(text)))
    else:
        raise ValueError('Not running on Python2 or Python 3?')


def load_vocab(vocab_file):
    """Loads a vocabulary file into a dictionary."""
    vocab = collections.OrderedDict()
    with tf.gfile.GFile(vocab_file, 'r') as reader:
        while True:
            token = convert_to_unicode(reader.readline())
            if not token:
                break
            token = token.strip().split()[0]
            if token not in vocab:
                vocab[token] = len(vocab)
    return vocab


def convert_by_vocab(vocab, items):
    """Converts a sequence of [tokens|ids] using the vocab."""
    output = []
    for item in items:
        output.append(vocab[item])
    return output


def convert_tokens_to_ids(vocab, tokens):
    return convert_by_vocab(vocab, tokens)


def convert_ids_to_tokens(inv_vocab, ids):
    return convert_by_vocab(inv_vocab, ids)


def whitespace_tokenize(text):
    """Runs basic whitespace cleaning and splitting on a piece of text."""
    text = text.strip()
    if not text:
        return []
    tokens = text.split()
    return tokens


class FullTokenizer(object):
    """Runs end-to-end tokenziation."""

    def __init__(self, vocab_file, do_lower_case = True, spm_model_file = None):
        self.vocab = None
        self.sp_model = None
        if spm_model_file:
            self.sp_model = spm.SentencePieceProcessor()
            tf.logging.info('loading sentence piece model')
            self.sp_model.Load(spm_model_file)
            # Note(mingdachen): For the purpose of consisent API, we are
            # generating a vocabulary for the sentence piece tokenizer.
            self.vocab = {
                self.sp_model.IdToPiece(i): i
                for i in range(self.sp_model.GetPieceSize())
            }
        else:
            self.vocab = load_vocab(vocab_file)
            self.basic_tokenizer = BasicTokenizer(do_lower_case = do_lower_case)
            self.wordpiece_tokenizer = WordpieceTokenizer(vocab = self.vocab)
        self.inv_vocab = {v: k for k, v in self.vocab.items()}

    def tokenize(self, text):
        if self.sp_model:
            split_tokens = encode_pieces(
                self.sp_model, text, return_unicode = False
            )
        else:
            split_tokens = []
            for token in self.basic_tokenizer.tokenize(text):
                for sub_token in self.wordpiece_tokenizer.tokenize(token):
                    split_tokens.append(sub_token)

        return split_tokens

    def convert_tokens_to_ids(self, tokens):
        if self.sp_model:
            # tf.logging.info('using sentence piece tokenzier.')
            return [
                self.sp_model.PieceToId(printable_text(token))
                for token in tokens
            ]
        else:
            return convert_by_vocab(self.vocab, tokens)

    def convert_ids_to_tokens(self, ids):
        if self.sp_model:
            # tf.logging.info('using sentence piece tokenzier.')
            return [self.sp_model.IdToPiece(id_) for id_ in ids]
        else:
            return convert_by_vocab(self.inv_vocab, ids)


class BasicTokenizer(object):
    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""

    def __init__(self, do_lower_case = True):
        """Constructs a BasicTokenizer.

    Args:
      do_lower_case: Whether to lower case the input.
    """
        self.do_lower_case = do_lower_case

    def tokenize(self, text):
        """Tokenizes a piece of text."""
        text = convert_to_unicode(text)
        text = self._clean_text(text)

        # This was added on November 1st, 2018 for the multilingual and Chinese
        # models. This is also applied to the English models now, but it doesn't
        # matter since the English models were not trained on any Chinese data
        # and generally don't have any Chinese data in them (there are Chinese
        # characters in the vocabulary because Wikipedia does have some Chinese
        # words in the English Wikipedia.).
        text = self._tokenize_chinese_chars(text)

        orig_tokens = whitespace_tokenize(text)
        split_tokens = []
        for token in orig_tokens:
            if self.do_lower_case:
                token = token.lower()
                token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token))

        output_tokens = whitespace_tokenize(' '.join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """Strips accents from a piece of text."""
        text = unicodedata.normalize('NFD', text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == 'Mn':
                continue
            output.append(char)
        return ''.join(output)

    def _run_split_on_punc(self, text):
        """Splits punctuation on a piece of text."""
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return [''.join(x) for x in output]

    def _tokenize_chinese_chars(self, text):
        """Adds whitespace around any CJK character."""
        output = []
        for char in text:
            cp = ord(char)
            if self._is_chinese_char(cp):
                output.append(' ')
                output.append(char)
                output.append(' ')
            else:
                output.append(char)
        return ''.join(output)

    def _is_chinese_char(self, cp):
        """Checks whether CP is the codepoint of a CJK character."""
        # This defines a "chinese character" as anything in the CJK Unicode block:
        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if (
            (cp >= 0x4E00 and cp <= 0x9FFF)
            or (cp >= 0x3400 and cp <= 0x4DBF)  #
            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
            or (cp >= 0xF900 and cp <= 0xFAFF)
            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
        ):  #
            return True

        return False

    def _clean_text(self, text):
        """Performs invalid character removal and whitespace cleanup on text."""
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xFFFD or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(' ')
            else:
                output.append(char)
        return ''.join(output)


class WordpieceTokenizer(object):
    """Runs WordPiece tokenziation."""

    def __init__(
        self, vocab, unk_token = '[UNK]', max_input_chars_per_word = 200
    ):
        self.vocab = vocab
        self.unk_token = unk_token
        self.max_input_chars_per_word = max_input_chars_per_word

    def tokenize(self, text):
        """Tokenizes a piece of text into its word pieces.

    This uses a greedy longest-match-first algorithm to perform tokenization
    using the given vocabulary.

    For example:
      input = "unaffable"
      output = ["un", "##aff", "##able"]

    Args:
      text: A single token or whitespace separated tokens. This should have
        already been passed through `BasicTokenizer.

    Returns:
      A list of wordpiece tokens.
    """

        text = convert_to_unicode(text)

        output_tokens = []
        for token in whitespace_tokenize(text):
            chars = list(token)
            if len(chars) > self.max_input_chars_per_word:
                output_tokens.append(self.unk_token)
                continue

            is_bad = False
            start = 0
            sub_tokens = []
            while start < len(chars):
                end = len(chars)
                cur_substr = None
                while start < end:
                    substr = ''.join(chars[start:end])
                    if start > 0:
                        substr = '##' + six.ensure_str(substr)
                    if substr in self.vocab:
                        cur_substr = substr
                        break
                    end -= 1
                if cur_substr is None:
                    is_bad = True
                    break
                sub_tokens.append(cur_substr)
                start = end

            if is_bad:
                output_tokens.append(self.unk_token)
            else:
                output_tokens.extend(sub_tokens)
        return output_tokens


def _is_whitespace(char):
    """Checks whether `chars` is a whitespace character."""
    # \t, \n, and \r are technically control characters but we treat them
    # as whitespace since they are generally considered as such.
    if char == ' ' or char == '\t' or char == '\n' or char == '\r':
        return True
    cat = unicodedata.category(char)
    if cat == 'Zs':
        return True
    return False


def _is_control(char):
    """Checks whether `chars` is a control character."""
    # These are technically control characters but we count them as whitespace
    # characters.
    if char == '\t' or char == '\n' or char == '\r':
        return False
    cat = unicodedata.category(char)
    if cat in ('Cc', 'Cf'):
        return True
    return False


def _is_punctuation(char):
    """Checks whether `chars` is a punctuation character."""
    cp = ord(char)
    # We treat all non-letter/number ASCII as punctuation.
    # Characters such as "^", "$", and "`" are not in the Unicode
    # Punctuation class but we treat them as punctuation anyways, for
    # consistency.
    if (
        (cp >= 33 and cp <= 47)
        or (cp >= 58 and cp <= 64)
        or (cp >= 91 and cp <= 96)
        or (cp >= 123 and cp <= 126)
    ):
        return True
    cat = unicodedata.category(char)
    if cat.startswith('P'):
        return True
    return False
